MCP server for GPT Researcher that gives Claude deep research, quick search, report writing, source retrieval, research context, and research-resource tools backed by web search and LLM providers.
Open the source and read safety notes before installing.
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
Decision playbook
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
0
63
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No baseline selected
No major trust-signal divergence detected in the current selection.
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Source provenance statusRequired
Marked as source-backed.
Metadata reviewed
No reviewed flag detected in metadata.
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Trust level risk gateRequired
Trust level does not block evaluation.
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Package verification flag
No package verification flag provided.
Checksum metadata
No checksum provided for downloaded artifact.
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
Add at least one more entry to compare trust differences.
Baseline comparison available
No baseline peer selected yet.
Diverging trust signals identified
No major trust-signal divergence found.
Setup at a glance
Copy-ready — paste the snippet to get started.
Adoption plan
Current risk score 24/100. Use staged verification before broader rollout.
Validate source and review signals before any execution.
Confirm source provenanceRequired
Source URL/provenance metadata is present.
Confirm metadata review state
No review metadata found; increase manual validation.
Verify install payload
Install/config payload exists and can be inspected.
Confirm safety, privacy, and package integrity signals.
Review safety notesRequired
Safety notes are present.
Review privacy notesRequired
Privacy notes are present.
Verify package integrity metadata
No package verification/checksum metadata.
Adopt in controlled steps based on the selected plan.
Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Evidence readiness
Missing required evidence: Metadata review. Risk score 31.
Source repository/provenance is listed.
Required in this preset
Review metadata is missing.
Required in this preset
Safety notes are present.
Required in this preset
Privacy notes are present.
Optional in this preset
Package integrity metadata is missing.
Optional in this preset
Install payload is available.
Required in this preset
Required gaps: Metadata review
Decision timeline
Blocking gaps: Check metadata review status. Risk 28.
triage
Source/provenance metadata is available.
triage
Review metadata is missing.
verify
Safety notes are available.
verify
Privacy notes are available.
verify
Package integrity metadata is missing.
rollout
Install payload is available.
Blockers: Check metadata review status
Prerequisite readiness
5 prerequisites to line up before setup. Have accounts and credentials ready first. Includes a review or approval gate.
Safety & privacy surface
5 safety and 5 privacy notes across 4 risk areas. Review closely: credentials & tokens, network access, third-party handling.
Disclosure: MIT-licensed open source MCP server for GPT Researcher. It requires separate LLM and search provider credentials, and usage may incur provider costs.
{
"mcpServers": {
"gptr-mcp": {
"command": "python",
"args": ["REPLACE_WITH_GPTR_MCP/server.py"],
"env": {
"OPENAI_API_KEY": "REPLACE_WITH_OPENAI_API_KEY",
"TAVILY_API_KEY": "REPLACE_WITH_TAVILY_API_KEY"
}
}
}
}GPT Researcher MCP Server connects Claude and other MCP clients to GPT Researcher through a dedicated MCP server repository. It exposes tools for deep web research, faster search, report writing, source retrieval, context retrieval, and reusable research resources.
Use it when Claude needs a supervised research workflow that can gather multiple sources, return citations, and produce a draft report instead of only returning a short web-search result list.
These sources were reviewed on 2026-06-06. Prefer the dedicated MCP repository, README, linked GPT Researcher upstream, license, server source, utilities, dependency list, environment example, Dockerfile, and compose file for current setup and behavior details.
deep_research for multi-source web research with source URLs and context.quick_search for faster search-result snippets.write_report.research://{topic} resource for reusable research context.research_query prompt to shape a research task and report goal.Clone the dedicated MCP server repository and install its Python dependencies:
git clone https://github.com/assafelovic/gptr-mcp.git
cd gptr-mcp
pip install -r requirements.txt
Set provider credentials in the MCP client configuration. For a local stdio
client, point to server.py:
{
"mcpServers": {
"gptr-mcp": {
"command": "python",
"args": ["REPLACE_WITH_GPTR_MCP/server.py"],
"env": {
"OPENAI_API_KEY": "REPLACE_WITH_OPENAI_API_KEY",
"TAVILY_API_KEY": "REPLACE_WITH_TAVILY_API_KEY"
}
}
}
}
The server can also run directly:
python server.py
Docker mode auto-detects container execution and switches to SSE transport on port 8000.
GPT Researcher MCP Server is a networked research tool. Treat every query as data that may be sent to model providers, search retrievers, and fetched web sources. Keep prompts free of secrets and review provider retention rules before using it for customer, legal, medical, financial, or internal strategy research.
For local use, protect MCP client config files that contain API keys. For Docker,
n8n, SSE, or Streamable HTTP deployments, bind only to trusted networks, isolate
containers, and avoid exposing research sessions or /messages endpoints without
an authentication layer.
Show that GPT Researcher MCP Server is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/mcp/gpt-researcher-mcp-server)GPT Researcher MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
| Field | MCP server for GPT Researcher that gives Claude deep research, quick search, report writing, source retrieval, research context, and research-resource tools backed by web search and LLM providers. Open dossier | No-key multi-engine MCP server, CLI, and local daemon for web search and public web content retrieval across engines such as Bing, DuckDuckGo, Brave, Exa, Baidu, CSDN, Juejin, Startpage, and Sogou. Open dossier | Self-hostable deep research app with MCP and SSE APIs for generating multi-step research reports using configurable LLM and search providers. Open dossier | Official Kagi MCP server that gives Claude web, news, video, podcast, image, and page-extraction tools backed by the Kagi Search and Extract APIs. Open dossier |
|---|---|---|---|---|
| Next steps | ||||
| Trust | ||||
| Review status | Not reviewed | Not reviewed | Not reviewed | Not reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| Submitter | oktofeesh1 | oktofeesh1 | oktofeesh1 | oktofeesh1 |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | — | |||
| Category | mcp | mcp | mcp | mcp |
| Source | Source-backed | Source-backed | Source-backed | Source-backed |
| Author | Assaf Elovic | Aas-ee | u14app | Kagi Search |
| Added | 2026-06-06 | 2026-06-06 | 2026-06-05 | 2026-06-06 |
| Platforms | ||||
| Harness | ||||
| Source repo | — | — | — | — |
| Safety notes | ✓GPT Researcher MCP Server sends research queries to configured search retrievers and LLM providers, which can create API costs and external data exposure. The server exposes `deep_research`, `quick_search`, `write_report`, source, context, prompt, and resource workflows that can gather and synthesize live web content. Docker mode auto-selects SSE transport on `0.0.0.0:8000`; bind it only on trusted networks and avoid exposing unauthenticated endpoints publicly. Generated reports can contain outdated, biased, incomplete, or hallucinated claims; review sources before acting on medical, legal, financial, or safety-critical output. Protect Claude Desktop or MCP client configuration files because they may contain API keys in the `env` block. | ✓open-webSearch can send search queries to multiple public engines and fetch public web pages, GitHub READMEs, CSDN articles, Juejin articles, and other supported targets. Search and fetch results can be incomplete, stale, rate-limited, blocked, region-dependent, or affected by search-engine ranking and scraping protections. Respect each target site's terms and robots expectations; do not use the server for abusive scraping, credentialed browsing, paywall bypass, or personal-data harvesting. The optional local daemon exposes HTTP endpoints for local tooling; keep it bound to trusted local interfaces and do not treat it as a public internet API. Proxy settings, Playwright WebSocket/CDP endpoints, and reused browser sessions can route traffic or cookies through external systems; configure them deliberately. Leave TLS verification enabled unless a specific target has a broken certificate chain and the risk has been accepted. | ✓Deep Research can make repeated model and search-provider calls, so set budgets, rate limits, and provider quotas before exposing it to broad agent workflows. Generated reports can contain stale, incomplete, or misinterpreted sources; require citation review before using output in legal, medical, financial, security, or customer-facing decisions. Bind Docker deployments to localhost unless a trusted reverse proxy or firewall is in front of the service, and set `ACCESS_PASSWORD` or equivalent gateway controls before enabling MCP access. Uploaded documents and local knowledge bases should be reviewed for copyright, sensitive data, and permission to process before research begins. | ✓Kagi MCP sends search queries, page URLs, filters, date ranges, lens IDs, and extraction requests to the Kagi API. Search and extraction calls can consume Kagi API quota or create API costs, especially when using high limits or inline page extraction. The `kagi_extract` tool fetches public page content as markdown; do not ask it to retrieve private, paywalled, or terms-restricted content unless you have permission. In HTTP mode, clients provide Kagi API keys through bearer tokens; expose the endpoint only behind trusted transport, access controls, and log redaction. The server supports configurable timeouts, retries, and hidden search parameters; tune these before giving broad agent access to current-web workflows. |
| Privacy notes | ✓Research queries, prompts, source URLs, fetched snippets, research context, generated reports, and cost metadata can enter the MCP client context. Provider APIs and search retrievers may receive sensitive research topics, entity names, customer details, or internal strategy questions. The server keeps in-process research IDs, context, source lists, and source URLs for later report/source/context calls during the session. Docker, n8n, SSE, or Streamable HTTP deployments can expose research sessions and messages to other systems on the network if not isolated. Local logs and troubleshooting output may include queries, errors, endpoint names, provider configuration issues, or session identifiers. | ✓Search queries, fetched URLs, result titles, snippets, article content, proxy URLs, browser endpoints, and fetched page text can be exposed to MCP clients, logs, and model context. Live search engines and fetched websites may observe queries, IP address, proxy exit, browser fingerprints, cookies, timing, and request headers. Playwright fallback or CDP reuse can expose browser state, existing cookies, logged-in sessions, or verification state to fetched pages. The project includes public URL validation and private-network target protections, but operators should still avoid fetching internal, secret, or customer-specific URLs. Redact sensitive search terms and downloaded page content before sharing transcripts or logs. | ✓Research prompts, uploaded files, generated reports, search queries, citations, model inputs, model outputs, provider API keys, access passwords, and deployment logs can contain sensitive data. Browser-local history and knowledge-base storage are local to the deployed app context, but server-side API mode can route data through the deployment host, model providers, and search providers. Review hosting logs, cache behavior, environment variable handling, and third-party provider retention before using Deep Research with private or regulated material. | ✓Kagi receives search queries, requested URLs, search filters, result domains, lens IDs, extraction targets, API keys, and request metadata. MCP clients and logs may store user questions, result snippets, extracted markdown, trace IDs, and error bodies returned by the Kagi API. Hosted HTTP deployments can process keys for multiple users; avoid server, proxy, and platform logs that record `Authorization` headers. Extracted pages may include personal data, copyrighted text, internal URLs, or sensitive context if the user provides those URLs. |
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| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Give Claude no-key multi-engine web search and public page fetching through open-webSearch with stdio, CLI, and optional local daemon modes.
Connect Claude to a self-hosted deep research service with Streamable HTTP or SSE MCP transport.
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